Jean-Marc Valin wrote: I see your point. The pauses between every couple of vocal emissions should be measured and taken into consideration in the large picture, just as the accent on each vocal emission, the probability of each sub-emission of representing a given phoneme, etc. Alas, I don't know a thing about voice recognition, except that I can play Chess on my Mac with voice alone. But I haven't booted MacOS X in a while :-D Cheers Toby

On Mon, 2005-06-06 at 10:43 -0700, Brad Arant wrote: Actually the interesting work is in the perception / cognitive psychology area. Once you have this the CS side is pretty simple. Lee

Jack O'Quin wrote: Actually, I happen to have done some research on the topic. I would have been really happy to give you some matlab and C code, but unfortunately, I am not allowed to publish the code under any open source license. What I can do is to give you some advice :) First, it is indeed a difficult problem. The first aspect is: do you need real time ? Should it be fast ? The second one are you allowed to have some training data (which allow a wide range of pattern recognition schemes: it is supervised learning) ? A threshold on the energy works as long as your signal is clear, and the speaker speaks with a more or less constant volume. One thing which is possible is to adapt the threshold dynamically, by using more or less clever schemes, such as minimum statistics. One thing which becomes obvious when you are working on this problem is that detecting voice is really difficult, but detecting voiced (ie vowels and some consonants) parts is easier. Many technics are based on the pitch (using features such as autocorrelation, cepstrum, etc...). Here is a bibliography which may help: Supervised technics (ie need a training set) : @INPROCEEDINGS{Basu2003, author = {Sumit Basu}, title = {A linked-hmm model for robust voicing and speech detection}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '03)}, year = {2003}, abstract = {We present a novel method for simultaneous voicing and speech detection based on a linked-HMM architecture, with robust features that are independent of the signal energy. Because this approach models the change in dynamics be- tween speech and non-speech regions, it is robust to low sampling rates, significant levels of additive noise, and large distances from the microphone. We demonstrate the perfor- mance of our method in a variety of testing conditions and also compare it to other methods reported in the literature.}, owner = {david}, keywords = {VAD}, } @INPROCEEDINGS{Enqing2002, author = {Dong Enqing and Liu Guizhong and Zhou Yatong and Zhang Xiaodi}, title = {Applying Support Vector Machine to Voice Activity Detection}, booktitle = {6th International Conference on Signal Processing Procedings (ICSP'02)}, year = {2002}, abstract = {A new voice activity detector (VAD) algorithm using support vector machines (SVM) is proposed in the paper, and the new VAD effectiveness is validated. The sequential minimal optimization (SMO) algorithm for fast training support vector machines is adopted. The proposed VAD algorithm via SVM (SVM-VAD) also uses the characteristic parameters set used by G.729 Annex B (G.729B) VAD. Comparing SVM-VAD with G729B VAD shows that it is effective for applying SVM to VAD. The new proposed VAD algorithm is integrated with G.729B instead of G.729B VAD, informal listening tests show that the integrated speech coding system has a little better efficiency over the G.729B VAD in perceptivity.}, owner = {david}, keywords = {VAD}, } Real time technics : @INPROCEEDINGS{Basu2001, author = {Sumit Basu and Brian Clarkson and Alex Pentland}, title = {Smart headphones: enhancing auditory awareness through robust speech detection and source localization}, booktitle = {Proceedings of the Int'l Conf. on Acoustics, Speech, and Signal Processing (ICASSP '01)}, year = {2001}, month = {May}, abstract = {We describe a method for enhancing auditory awareness by selec- tively passing speech sounds in the environment to the user. We develop a robust far-eld speech detection algorithm for noisy en- vironments and a source localization algorithm for exible arrays. We then combine these methods to give a user control over the spatial regions from which speech will be passed through. Using this technique, we have implemented a ¡Èsmart headphones¡É system in which a user can be listening to music over headphones and hear speech from specied directions mixed in. We show our prelim- inary results on the algorithms and describe initial user feedback about the system.}, owner = {david}, keywords = {VAD Ubiquitous}, } @INPROCEEDINGS{Zhang2002, author = {Jianping Zhang, Wayne Ward and Bryan Pellom}, title = {Phone Based Voice Activity Detection Using Online Bayesian Adapation with Conjugate Normal Distributions}, booktitle = {ICASSP02}, year = {2002}, organization = {IEEE}, abstract = {In this paper, we developed a highly efcient frame-level on- line adaptive voice activity detection (VAD) algorithm for the telephone-based CU Communicator spoken dialog system. The adaptive algorithm uses prior speaker and channel statistics as well as acoustic features of current sample frames to update model pa- rameters. The algorithm achieved .05xRT in contrast to .7xRT of a compared VAD algorithm using 5-state HMMs. We detail the adaptive algorithm and address some real-time implementation is- sues. Experiments on live collected data show that there is a 23% error reduction compared with G.729B VAD.}, owner = {david}, keywords = {VAD}, } @INPROCEEDINGS{Li2001, author = {Qi Li and Jinsong Zheng and Qiru Zhou and Chin-Hui Lee}, title = {A Robust, Real-Time Endpoint Detector with Energy Normalization for ASR in Adverse Environments}, booktitle = {ICASSP01}, year = {2001}, organization = {IEEE}, abstract = {When automatic speech recognition (ASR) is applied to hands-free or other adverse acoustic environments, endpoint detection and energy normalization can be crucial to the en- tire system. In low signal-to-noise (SNR) situations, conven- tional approaches of endpointing and energy normalization often fail and ASR performances usually degrade dramati- cally. The goal of this paper is to nd a fast, accurate, and robust endpointing algorithm for real-time ASR. We pro- pose a novel approach of using a special lter plus a 3-state decision logic for endpoint detection. The lter has been designed under several criteria to ensure the accuracy and robustness of detection. The detected endpoints are then applied to energy normalization simultaneously. Evaluation results show that the proposed algorithm signicantly reduce the string error rates on 7 out of 12 tested databases. The reduction rates even exceeded 50% on two of them. The algorithm only uses one-dimensional energy with 24-frame lookahead; therefore, it has a low complexity and is suitable for real-time ASR.}, owner = {david}, keywords = {VAD}, } Non real time algorithms (ie need several passes): @ARTICLE{Spanias1999, author = {Sassan Ahmadi, Andreas S. Spanias}, title = {Cepstrum-Based Pitch Detection Using a New Statistical V/UV classification algorithm}, journal = {IEEE Transactions on Speech and Audio Processing}, year = {1999}, volume = {7}, number = {3}, pages = {333-338}, month = {May}, owner = {david}, keywords = {none VAD }, abstract = {An improved cepstrum-based voicing detection and pitch determination algorithm is presented. Voicing decisions are made using a multifeature voiced/unvoiced classication algorithm based on statistical analysis of cepstral peak, zero-crossing rate, and energy of short-time segments of the speech signal. Pitch frequency information is extracted by a modied cepstrum-based method and then carefully rened using pitch tracking, correction, and smoothing algorithms. Performance analysis on a large database indicates considerable improvement relative to the conventional cepstrum method. The proposed algorithm is also shown to be robust to additive noise.}, } Cheers, David